The article aims at identifying problems of the use of artificial neural networks for
solving problems of binary classification. For solving problems of binary classification it is
expected to classify samples that are already available to a certain class. A leading approach
to the study of this problem is the algorithm of Levenberg-Marquardt, which allows to
optimize the parameters of nonlinear regression models. As optimization criterion is adopted
to the root mean square error of the model on the training set. It is proposed to accelerate the
computation to apply the method to the elastic distribution. The materials of the article are of
practical value to professionals who use artificial neural networks for classification tasks.
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Murashkin Nikita Gennadievich
Voronezh Institute of High Technologies
Voronezh, Russian Federation
Kostrova Vera Nikolaevna
Doctor of Technical Sciences Professor
Email: vn-kostrova@vivt.ru
Voronezh State Technical University
Voronezh, Russian Federation
